Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data

A gridded population dataset was produced for Pakistan by developing an algorithm that distributed population either on the basis of per-pixel built-up area fraction or the per-pixel value of a weighted population likelihood layer. Per-pixel built-up area fraction was calculated using a classification and regression trees (CART) methodology integrating high- and medium-resolution satellite imagery. The likelihood layer was produced by weighting different geospatial layers according to their effect on the likelihood of population being found in the particular pixel. The geospatial layers integrated into the likelihood layer were: 1) proximity to remotely sensed built-up pixels, 2) density of settlement points in a fixed kernel, 3) slope, 4) elevation, and 5) heterogeneity of landcover types found within a search radius. The method for weighting these layers varied according to settlement patterns found in the provinces of Pakistan. Differences in zonal population estimates generated from the 100-meter gridded population layer resulting from this study, Oak Ridge National Laboratory's LandScan (2002), and CIESIN's Gridded Population of the World and Global Rural Urban Mapping Project (GPW and GRUMP) are examined. Population estimates for small areas produced using this paper's method were found to differ from census counts to a lesser degree than those produced using LandScan, GPW, or GRUMP. The root mean square error (RMSE) for small area population estimates for this method, LandScan, GPW, and GRUMP were 31,089, 48,001, 100,260, and 72,071, respectively. Published by Elsevier Inc.